You can search for me in Linked In by my name as Leonard De Lanerole. I’m a Data Engineer specialising in Snowflake, dbt, Matillion, and Azure, with hands‑on experience delivering scalable analytics platforms across sports, telecom, and enterprise environments. I design and build end‑to‑end data solutions that transform raw data into trusted, analytics‑ready insights. My core expertise includes Snowflake and dbt Cloud modelling, Amazon Redshift data warehousing (raw, curated, and reporting layers), and Matillion ETL for reliable batch pipelines. I’ve worked extensively with Azure Data Factory for orchestration, Azure Logic Apps for integrations and automation, and Power BI for delivering business‑ready reporting. I am Microsoft Azure certified and experienced in production support, performance optimisation, and building maintainable data architectures. I bring a practical, detail‑driven approach and can own data pipelines end‑to‑end—from ingestion to analytics and reporting.

I help businesses turn messy operational and commercial data into reliable reporting models, trusted KPIs, and decision-ready dashboards. I’m an Analytics Engineer / BI Specialist with 5+ years of experience across hospitality, healthcare, and supply chain, working with SQL, Power BI, Tableau, Python, dbt, PostgreSQL, dimensional modeling, and Microsoft Fabric. My work is best suited for teams that need more than a dashboard. I build the data foundation behind reporting—from SQL logic and transformation workflows to KPI standardization, semantic modeling, and executive-ready reporting. What I can help with: • Analytics engineering for BI and reporting • SQL development for transformation, reporting, and performance analysis • dbt models and analytics workflows • Power BI dashboards, semantic models, and KPI reporting • Dimensional modeling using star schema, fact tables, and dimension tables • Reporting automation to reduce manual work • Operational and commercial analytics • Forecasting, anomaly analysis, and root-cause analysis • Data quality checks and reporting consistency improvements Selected impact: • Built executive Power BI dashboards covering 50+ hotel properties • Contributed to ~7% reduction in energy-related costs through anomaly analysis using Python • Standardized KPI definitions across business teams to improve reporting consistency • Automated recurring operational reporting to improve turnaround time and reduce manual effort Certifications: • Microsoft Certified: Fabric Analytics Engineer Associate (DP-600) • Microsoft Certified: Power BI Data Analyst Associate (PL-300)

AI & Data Engineer Sometimes a business has the perfect product, the perfect team, and the perfect strategy — but still struggles. Not because what they offer is wrong… but because their data isn’t structured, optimized, or automated in a way that supports growth I’ve seen companies sitting on massive value while drowning in messy spreadsheets, broken pipelines, inconsistent KPIs, slow dashboards, or unreliable reporting. The problem isn’t what they’re selling — it’s how their data is being captured, processed, and transformed That’s where I come in As an AI-Driven Data Engineer & Analytics Specialist with 2+ years of experience, I help businesses turn raw, chaotic data into clean, automated, AI-powered systems that drive real decisions, real efficiency, and real revenue Great data infrastructure doesn’t just look good in a dashboard 👉 it grabs attention, powers insights, and drives smarter, faster business growth 👉 WHAT I OFFER (AI, Data Engineering & Analytics) ⚡ ETL/ELT Pipelines – Automated, scalable pipelines using SSIS, Airflow, Airbyte, Python & SQL. ⚡ Data Warehousing – STG/ODS/DWH design using SQL Server, Snowflake, ClickHouse, and Azure Synapse ⚡ Real-Time Data Streaming – Kafka & Spark pipelines for fast, reliable event processing ⚡ Business Intelligence Dashboards – Power BI & Tableau dashboards that turn data into decisions ⚡ Data Modeling (OLTP → OLAP) – Star/Snowflake schemas that optimize analytics performance ⚡ Cloud Data Engineering – Azure Data Factory, Databricks, Snowflake, dbt & modern MDS architectures ⚡ AI-Assisted Automation – Data validation, anomaly detection, SQL generation, documentation & workflow optimization ⚡ Data Quality Frameworks – Automated testing, monitoring & CI/CD-driven data workflows 👉 EXTENSIVE EXPERIENCE IN THE FOLLOWING ⚡ AI Data Workflows – Integrating LLMs for faster validation, optimization & documentation ⚡ DataOps Practices – CI/CD, version control, observability & automated QA for data pipelines ⚡ Modern Data Stack – Airflow, Airbyte, dbt, ClickHouse, Snowflake, Databricks, Synapse ⚡ Big Data Processing – Spark, PySpark, Kafka streaming, and distributed compute ⚡ Visual Analytics – KPI modeling, dashboard design, and analytical storytelling ⚡ Cloud Architecture – Azure ecosystem (ADF, ADLS, Synapse, Databricks) ⚡ End-to-End Data Solutions – From ingestion → transformation → modeling → analytics → automation ✨ TOOLS I USE ⚡ Python & SQL – Core engines for transformation, validation & analytics ⚡ Airflow – Orchestration of complex workflows ⚡ Airbyte – Connector-based ingestion from multiple sources ⚡ dbt – ELT modeling, testing, documentation & lineage ⚡ Azure Data Factory – Cloud orchestration and pipelines at scale ⚡ Databricks (PySpark/Delta) – High-performance data processing ⚡ SQL Server / SSIS / SSAS – Enterprise-grade warehousing and OLAP analytics ⚡ Power BI & Tableau – Turning data into high-value insights ⚡ ClickHouse – Ultra-fast analytical storage ⚡ Git & Azure DevOps – CI/CD, automation, and data versioning 🎯 Final Message Your business doesn’t just need dashboards — It needs data systems that work, pipelines that never break, models that scale, and AI-driven workflows that save hours and multiply output If you’re ready to build powerful, automated, and intelligent data infrastructure that increases performance, accuracy, and revenue… 📩 Send me a message and let’s build something extraordinary

Senior Data Engineer with 3+ years of experience designing high-volume ETL/ELT pipelines, data models, and analytics-ready platforms using Python, SQL, Spark, dbt, Airflow, and AWS. I build reliable, well-tested data systems that teams can trust. My work spans medallion/lakehouse architectures, schema testing, data validation, lineage documentation, SLA monitoring, and AI-assisted development with Claude Code and Cursor. What I deliver: - High-volume ETL/ELT pipelines processing 2-3M+ records monthly - Dimensional models and dbt transformation layers supporting 50+ KPI definitions - Medallion / lakehouse architectures on Snowflake, Databricks, and AWS - Automated data quality checks, schema testing, and freshness monitoring - Pipeline optimization (reduced runtimes from 90 to 55 minutes; models from 10GB to 1GB) - Clear documentation of pipeline designs, KPI definitions, and dbt lineage Selected results: - Reduced reconciliation defects from 70 to fewer than 15 per cycle - Cut source discrepancies from thousands to single-digit mismatches - Reduced manual QA effort from 6-7 hours to 2-3 hours per cycle - Enabled routing improvements that reduced late deliveries by 18% Tech stack: Python, SQL, Apache Spark, dbt, Airflow, Snowflake, Databricks, AWS (Lambda), Pandas, scikit-learn, REST APIs, Git/CI-CD, Claude Code, Cursor. If you need dependable datasets, transformation layers, and operational pipelines for analytics or AI-driven workflows, let's talk.

I’m a Senior Analytics Engineer specializing in Snowflake, dbt, SQL, and Python, with experience building scalable analytics platforms for a fintech company. I help businesses design reliable data pipelines, optimize Snowflake performance, and build clean, maintainable data models that power reporting and business decision-making. I can help with: • Snowflake data warehouse development and optimization • dbt project development and maintenance • SQL query optimization and performance tuning • ETL/ELT pipeline development • Python automation and data integration • Data modelling for analytics • Data quality testing and documentation In my current role, I work on production analytics systems that support lending and financial reporting. My day-to-day work includes building dbt models, developing scalable ELT pipelines, improving query performance, and collaborating with cross-functional teams to deliver trusted data products. My technical stack includes: • Snowflake • dbt • SQL • Python • Git • PostgreSQL • Data Warehousing • ETL / ELT Whether you need help building a new data pipeline, optimizing an existing Snowflake environment, or improving your analytics workflow, I focus on delivering clean, maintainable, and well-documented solutions.

I build AWS, GCP, and Azure data pipelines that marketing and SaaS teams query daily — clean, documented, and reliable. My last pipeline turned 30+ Meta Ads streams into a star schema a growth team runs every morning. Recent production work: • Meta Ads → BigQuery/PostgreSQL pipeline (Airbyte → S3 → PySpark + Apache Hudi) with SCD2 dimensional modeling, SHA-256 deduplication, and late-arriving attribution handling. • dbt + BigQuery + Looker Studio analytics on 99K+ orders — 7 staging models, 6 marts, 28 data-quality tests, 4-page dashboard. • Real-time Kafka + PySpark streaming pipeline running 9 concurrent queries with 4 windowed aggregations into PostgreSQL. • Full AWS data platform on Terraform + EKS + EMR-on-EKS + Airflow with CI/CD. How I work: I document every architectural decision before I build — scope, schema, and tradeoffs written down first — so downstream teams know exactly what they're querying. You get async-friendly updates on US/EU hours and pipelines that don't silently break on schema drift or duplicate events. Core stack: Python, PySpark, SQL, Apache Airflow, dbt, Apache Hudi, Apache Kafka, AWS (S3, Glue, EMR, EKS, Athena), GCP/BigQuery, PostgreSQL, ClickHouse, Snowflake, Docker, Terraform, Looker Studio. Focus: ETL/ELT pipelines, dimensional modeling, data warehousing, streaming, and marketing analytics. If you're building a data pipeline or need messy data turned into something your team can trust, send me the brief — I reply within 24 hours.

Imagine this: Your company has valuable data, but pipelines are messy, workflows are manual, and analytics are slow. Decisions are made on guesswork instead of facts. The result? Wasted time, missed opportunities, and stalled growth. That’s where I come in. I help businesses build scalable, automated data pipelines that turn raw data into actionable insights—so you can make decisions confidently and efficiently. ✨ WHAT I OFFER: ✔ Design and automation of ETL pipelines that clean, transform, and load your data reliably ✔ Workflow orchestration with Airflow to reduce repetitive tasks and errors ✔ Data modeling with dbt for clean, maintainable datasets ✔ Scalable cloud analytics with Snowflake ✔ Python & SQL scripting for data extraction, transformation, and analysis ✨ EXTENSIVE EXPERIENCE IN THE FOLLOWING: 🔹 Building and automating ETL/ELT pipelines 🔹 Data cleaning, transformation, and integration 🔹 Workflow automation and orchestration 🔹 Data modeling and structuring for analytics 🔹 Cloud data warehousing and scalable storage 🔹 Python & SQL scripting for complex operations 🔹 Creating interactive dashboards and reports ✨ TOOLS & TECHNOLOGIES I USE: 🛠Python (Pandas, NumPy) 🛠SQL (PostgreSQL, MySQL, Snowflake SQL) 🛠dbt for modern data modeling 🛠Airflow for workflow orchestration 🛠 Snowflake for cloud data warehousing 🛠 Power BI & Tableau for dashboards and visualizations I focus on accuracy, automation, and scalability, helping you transform your data into insights that support growth, efficiency, and smarter business decisions.

Are you struggling to turn raw data into clear, actionable insights? I help businesses build reliable, scalable data pipelines and analytics platforms — so you spend less time wrestling with data and more time making decisions. I’m a Data & Analytics Engineer with hands-on experience delivering end-to-end data solutions using Azure , Snowflake and Databricks. From ingestion to visualization, I build systems that are efficient, maintainable, and business-focused. What I Can Help You With? - Building ETL/ELT pipelines - Architecting cloud data warehouses - Real-time & batch data ingestion - Dimensional modeling for analytics-ready data marts - Creating insightful dashboards with Power BI What You Get - Clean, well-structured data you can trust - Scalable solutions that grow with your business - Faster access to insights and better decision-making - Clear communication and reliable delivery

Data Analyst with experience in analysing business performance and operational metrics across digital products and web-based services. Skilled in SQL, Python, and dbt for building analytics-ready datasets. Experienced in statistical analysis, basic machine learning, KPI design, and behavioural segmentation to identify trends, support decision-making, and improve business performance. 𝐈’𝐦 𝐩𝐚𝐫𝐭𝐢𝐜𝐮𝐥𝐚𝐫𝐥𝐲 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐞𝐝 𝐢𝐧 𝐫𝐨𝐥𝐞𝐬 𝐰𝐡𝐞𝐫𝐞 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐮𝐬𝐞𝐝 𝐭𝐨: • Improve service delivery, onboarding, and retention; • Understand user behaviour and operational workflows; • Build reliable data models; and • Support product, operations, and growth decisions. If your team works with behavioural data, digital products, web-based services, or related fields, I'd be glad to connect.

A failing data pipeline is usually not the complete problem. The deeper issue may be poor architecture, inconsistent source data, missing validation, weak orchestration, slow SQL models, uncontrolled cloud costs, or a platform that was never designed to scale. I help companies identify the real constraint and build a data system that is reliable, observable, scalable, and useful to the business. With 8+ years of experience across data engineering and cloud infrastructure, I support organizations that need to: 📉 Eliminate recurring pipeline failures ⏱️ Reduce reporting delays and manual processing 🔗 Integrate disconnected applications, APIs, and databases 📊 Create trustworthy datasets for dashboards and analytics ☁️ Modernize legacy infrastructure in AWS, GCP, or Azure 🤖 Prepare structured data for machine learning and AI 💰 Improve performance without wasting cloud resources 🚀 How I Solve Data Problems 🔎 Discovery and Diagnosis I review your existing architecture, data sources, pipelines, reporting requirements, failure points, processing volumes, and business priorities. The objective is to determine whether the real bottleneck is ingestion, transformation, modeling, orchestration, infrastructure, data quality, or downstream reporting. 🏗️ Architecture and Implementation Based on the diagnosis, I design a practical solution that may include: * Batch or real-time ingestion pipelines * Cloud data lakes, warehouses, or lakehouses * ETL/ELT orchestration and automation * Dimensional and analytics-ready data models * Data validation and quality controls * Monitoring, alerts, retries, and failure recovery * Infrastructure as Code and CI/CD * Performance and cost optimization 🛡️ Stabilization and Accountability Delivery does not end when the code runs once. I focus on production readiness through documentation, testing, logging, monitoring, deployment processes, ownership clarity, and maintainable architecture. 🛠️ Technical Expertise Python | SQL | PySpark | Apache Airflow | Apache Spark | Kafka | dbt | Databricks | AWS Glue | Google Dataflow | BigQuery | Snowflake | Redshift | Azure Synapse | Docker | Kubernetes | Terraform | GitHub Actions | Power BI | Tableau | MLflow 💼 What You Receive ✅ A clear understanding of the root problem ✅ An architecture aligned with your actual requirements ✅ Reliable and maintainable production pipelines ✅ Clean, validated, analytics-ready datasets ✅ Monitoring and visibility into pipeline health ✅ Documentation your internal team can understand ✅ A scalable foundation for reporting, automation, and AI I work as a technical partner not an order taker. Instead of simply implementing the first tool requested, I help determine what should be built, why it should be built, and how it will improve reliability, speed, cost, or decision-making. 📩 Share your current challenge, architecture, and expected outcome. I’ll help you identify the most important issue to solve first.